The global Landsat imagery database for the FAO FRA remote sensing survey

Abstract To collect and provide periodically updated information on global forest resources, their management and use, the United Nations Food and Agriculture Organization (FAO) has been coordinating global forest resources assessments (FRA) every 5–10 years since 1946. To complement the FRA national-based statistics and to provide an independent assessment of forest cover and change, a global remote sensing survey (RSS) has been organized as part of FAO FRA 2010. In support of the FAO RSS, an image data set appropriate for global analysis of forest extent and change has been produced. Landsat data from the Global Land Survey 1990–2005 were systematically sampled at each longitude and latitude intersection for all points on land. To provide a consistent data source, an operational algorithm for Landsat data pre-processing, normalization, and cloud detection was created and implemented. In this paper, we present an overview of the data processing, characteristics, and validation of the FRA RSS Landsat dataset. The FRA RSS Landsat dataset was evaluated to assess overall quality and quantify potential limitations.

[1]  M. Wimberly,et al.  Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery , 2007 .

[2]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[3]  F. Achard,et al.  Tropical forest cover change in the 1990s and options for future monitoring , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[4]  J. Goldammer.  Global Forest Resources Assessment 2005 – Thematic report on forest fires in the Central Asian Region and adjacent countries / FAO Fire Management Working Paper 16 , 2006 .

[5]  F. Achard,et al.  Determination of Deforestation Rates of the World's Humid Tropical Forests , 2002, Science.

[6]  C. Tucker,et al.  NASA’s Global Orthorectified Landsat Data Set , 2004 .

[7]  P. Strobl,et al.  Pan-European Forest/Non-Forest Mapping with Landsat ETM+ and CORINE Land Cover 2000 Data , 2009 .

[8]  S. Stehman,et al.  Statistical sampling to characterize recent United States land-cover change , 2003 .

[9]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[10]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[11]  Ronald J. Hall,et al.  Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program , 2003 .

[12]  S. Goward,et al.  Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm , 2006 .

[13]  C. Justice,et al.  Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005 , 2008 .

[14]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[15]  Simonetti Dario,et al.  Global Monitoring of Tropical Forest Cover Changes by Means of a Sample Approach and Object-based Classification of Multi-scene Landsat Imagery: Pre-processing and First Results , 2009 .

[16]  Stephen V. Stehman,et al.  An evaluation of sampling strategies to improve precision of estimates of gross change in land use and land cover , 2005 .

[17]  Christopher O. Justice,et al.  Land Remote Sensing and Global Environmental Change , 2011 .

[18]  J. Townshend,et al.  Rapid loss of Paraguay's Atlantic forest and the status of protected areas — A Landsat assessment , 2007 .

[19]  Achard Frederic,et al.  The 2010 Global Forest Resource Assessment Remote Sensing Survey , 2009 .

[20]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[21]  A. Grainger.  Difficulties in tracking the long-term global trend in tropical forest area , 2008, Proceedings of the National Academy of Sciences.

[22]  Stephen V. Stehman,et al.  A comparison of sampling designs for estimating deforestation from Landsat imagery: A case study of the Brazilian Legal Amazon , 2009 .

[23]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[24]  D. Roy,et al.  A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin , 2008 .

[25]  Piermaria Corona,et al.  VALUTAZIONE DELLE RISORSE FORESTALI A LIVELLO GLOBALE , 2013 .

[26]  Simonetti Dario,et al.  Monitoring Forest Cover at Global Scale: The JRC Approach , 2009 .

[27]  Rasim Latifovic,et al.  Selecting Representative High Resolution Sample Images for Land Cover Studies. Part 1: Methodology , 2000 .

[28]  Gregory Duveiller,et al.  Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts , 2008 .

[29]  Curtis E. Woodcock,et al.  Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors , 2001 .

[30]  Rasim Latifovic,et al.  Selecting Representative High Resolution Sample Images for Land Cover Studies. Part 2 Application to Estimating Land Cover Composition , 2000, Remote Sensing of Environment.

[31]  J. Townshend,et al.  MODIS Vegetative Cover Conversion and Vegetation Continuous Fields , 2010 .

[32]  J. Townshend,et al.  Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data , 2008, Proceedings of the National Academy of Sciences.

[33]  United Kingdom,et al.  GLOBAL FOREST RESOURCES ASSESSMENT 2005 , 2005 .

[34]  Stephen V. Stehman,et al.  Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss , 2008 .

[35]  P. Tickle,et al.  Global ecological zoning for the Global Forest Resources Assessment 2000 - Final report , 2001 .

[36]  R. Czaplewski,et al.  Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation? , 2003 .

[37]  Ranz,et al.  World Map of the Köppen-Geiger climate classification updated — Source link , 2006 .

[38]  N. Gemini,et al.  An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data* , 1988 .

[39]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[40]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .